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Robust surface states are a defining feature of topological photonic and acoustic systems. Although the supercell technique is commonly used for studying their surface effects, it falls short in sorting the states at two boundaries, differentiating surface states from bulk states, and demanding large computational resources. To overcome the limitations, we employ surface Green's functions combined with two efficient algorithms to obtain the ideal surface states for a semi-infinite system. They are further extended to more complex scenarios, such as coated structures, heterostructures, and sandwiched structures. Photonic Chern insulators, valley photonic crystals, and acoustic topological insulators are taken as examples for demonstration and verification. The surface spectra obtained from our approach can be directly used for comparison with experimental observables. Our approach enriches the efficient computational tools for topological photonics and acoustics., Comment: 11 pages, 7 figures

Motivated by recent experimental observations of Kondo resonances in cobalt atoms on single layer 1T-TaSe$_{2}$, we theoretically investigate the effect of coupling a U(1) quantum spin liquid with a spinon Fermi surface to a lattice of Anderson impurities. Within the slave-rotor formalism, we find that above a critical coupling strength between the spin liquid and impurity lattice, the spinons hybridize to form heavy quasiparticles near the Fermi level, realizing a {\it spinon Kondo lattice phase} analogous to heavy fermion materials. Using the Bethe-Salpeter equation and accounting for emergent gauge fluctuations, we compute the spectral density and density of states, revealing the formation of spinon-chargon bound states in the spinon Kondo lattice phase. We characterize the thermodynamic and spectroscopic signatures of this phase, demonstrating specific heat and neutron scattering responses distinct from a pure quantum spin liquid. Our findings establish the spinon Kondo lattice as a framework to study the rich physics of spin liquids., Comment: 12 pages, 7 figures

Topological magnons in a one-dimensional (1D) ferromagnetic (FM) Su-Schrieffer-Heeger (SSH) model with anisotropic exchange interactions are investigated. Apart from the inter-cellular isotropic Heisenberg interaction, the intercellular anisotropic exchange interactions, i.e. Dzyaloshinskii-Moriya interaction (DMI) and pseudo-dipolar interaction (PDI), also can induce the emergence of the non-trivial phase with two degenerate in-gap edge states separately localized at the two ends of the 1D chain, while the intracellular interactions instead unfavors the topological phase. The interplay among them has synergistic effects on the topological phase transition, very different from that in the two-dimensional (2D) ferromagnet. These results demonstrate that the 1D magnons possess rich topological phase diagrams distinctly different from the electronic version of the SSH model and even the 2D magnons. Due to the lower dimensional structural characteristics of this 1D topological magnonic system, the magnonic crystals can be constructed from bottom to top, which has important potential applications in the design of novel magnonic devices., Comment: 22 pages, 11 figures

This paper proposes a general analytical approach to derive the outage probability of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over correlated fading channels in closed-form. Unlike prior analyses, the consideration of channel correlation is one of the key reasons making the outage analysis involved. Using conditional Mellin transform, the outage probability of HARQ-IR over correlated Rayleigh fading channels is exactly expressed as a mixture of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weights are negative multinomial probabilities. Its straightforward application is to conduct asymptotic outage analysis to gain more insights, in which the asymptotic outage probability is obtained in a concise form. The asymptotic outage probability possesses some special properties which ease optimal power allocation and rate selection of HARQ-IR. Finally, numerical results are presented for validations and discussions.

For image forensics, convolutional neural networks (CNNs) tend to learn content features rather than subtle manipulation traces, which limits forensic performance. Existing methods predominantly solve the above challenges by following a general pipeline, that is, subtracting the original pixel value from the predicted pixel value to make CNNs pay attention to the manipulation traces. However, due to the complicated learning mechanism, these methods may bring some unnecessary performance losses. In this work, we rethink the advantages of gradient operator in exposing face forgery, and design two plug-and-play modules by combining gradient operator with CNNs, namely tensor pre-processing (TP) and manipulation trace attention (MTA) module. Specifically, TP module refines the feature tensor of each channel in the network by gradient operator to highlight the manipulation traces and improve the feature representation. Moreover, MTA module considers two dimensions, namely channel and manipulation traces, to force the network to learn the distribution of manipulation traces. These two modules can be seamlessly integrated into CNNs for end-to-end training. Experiments show that the proposed network achieves better results than prior works on five public datasets. Especially, TP module greatly improves the accuracy by at least 4.60% compared with the existing pre-processing module only via simple tensor refinement. The code is available at: https://github.com/EricGzq/GocNet-pytorch.